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cs.CR updates on arXiv.org

On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety
SOBA: Secrecy-preserving Observable Ballot-level Audit
Josh Benaloh, Douglas Jones, Eric Lazarus, Mark Lindeman, Philip · 2011-05-30 · via cs.CR updates on arXiv.org

SOBA is an approach to election verification that provides observers with justifiably high confidence that the reported results of an election are consistent with an audit trail ("ballots"), which can be paper or electronic. SOBA combines three ideas: (1) publishing cast vote records (CVRs) separately for each contest, so that anyone can verify that each reported contest outcome is correct, if the CVRs reflect voters' intentions with sufficient accuracy; (2) shrouding a mapping between ballots and the CVRs for those ballots to prevent the loss of privacy that could occur otherwise; (3) assessing the accuracy with which the CVRs reflect voters' intentions for a collection of contests while simultaneously assessing the integrity of the shrouded mapping between ballots and CVRs by comparing randomly selected ballots to the CVRs that purport to represent them. Step (1) is related to work by the Humboldt County Election Transparency Project, but publishing CVRs separately for individual contests rather than images of entire ballots preserves privacy. Step (2) requires a cryptographic commitment from elections officials. Observers participate in step (3), which relies on the "super-simple simultaneous single-ballot risk-limiting audit." Step (3) is designed to reveal relatively few ballots if the shrouded mapping is proper and the CVRs accurately reflect voter intent. But if the reported outcomes of the contests differ from the outcomes that a full hand count would show, step (3) is guaranteed to have a large chance of requiring all the ballots to be counted by hand, thereby limiting the risk that an incorrect outcome will become official and final.